Changes in the physical, chemical, and microbiological structure of yogurt determine the storage and shelf life of the product. In this study, microbial counts and pH values of yogurt during storage were determined at d 1, 7, and 14. Simultaneously, image processing of yogurt was digitized by using a machine vision system (MVS) to determine color changes during storage, and the obtained data were modeled with an artificial neural network (ANN) for prediction of shelf life of set-type whole-fat and low-fat yogurts. The ANN models were developed using back-propagation networks with a single hidden layer and sigmoid activation functions. The input variables of the network were pH; total aerobic, yeast, mold, and coliform counts; and color analysis values measured by the machine vision system. The output variable was the storage time of the yogurt. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.9996) showing that the developed model was able to analyze nonlinear multivariant data with very good performance, fewer parameters, and shorter calculation time. The model might be an alternative method to control the expiration date of yogurt shown in labeling and provide consumers with a safer food supply.
The aim of this study was to evaluate the bacterial ecosystem of milk and Ezine cheese by PCR amplification of the V3 region of the bacterial 16S rRNA gene followed by denaturing gradient gel electrophoresis (DGGE) and by monitoring the bacterial diversity dynamics using PCR single‐strand conformation polymorphism (SSCP) analysis. PCR‐DGGE analysis revealed that 17 different bands and strains belonging to the Lactococcus lactis group and Streptococcus thermophilus were predominant during manufacturing and ripening. SSCP analysis revealed that the bacterial profiles of the two cheese samples were similar.
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